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dc.contributor.authorBudiman, Edy
dc.contributor.authorHaryaka, Usfandi
dc.contributor.authorRoosevelt Watulingas, Jefferson
dc.contributor.authorAlameka, Faza
dc.date.accessioned2020-08-18T14:46:18Z
dc.date.available2020-08-18T14:46:18Z
dc.date.issued2017-09-19
dc.identifier.citationE. Budiman, U. Haryaka, J. R. Watulingas and F. Alameka, "Performance rate for implementation of mobile learning in network," 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Yogyakarta, 2017, pp. 1-6, doi: 10.1109/EECSI.2017.8239187.en_US
dc.identifier.isbn978-1-5386-0549-3
dc.identifier.urihttp://repository.unmul.ac.id/handle/123456789/4903
dc.description.abstractThis paper discusses the availability of mobile networks and develops mobile learning software. The measurement Approach is testing from the user experience side to some point where potential users are located. Data clustering is divided into six location of measurement points, with different time sessions; morning, daylight, afternoon, and evening. In each time session, the measurement process is done as much as 10 times test for each the card service packs from Internet Service Providers (ISPs). The measurement process is carried out continuously for 21 days (three weeks), this is done to ensure the availability of mobile networks in the location. The results of measurement and application testing, have given conclusion and contribution, that in of the research explains that although performance levels such as download and upload speed, latency, jitter and packet loss metrics are appropriate, not necessarily the level of network availability is appropriate. Because each parameter is influenced by a certain factor. The average value of network availability measurement experienced an internet connection failure rate on one of ISP 84.046% or as many as 10 to 11 times failed to connect from 70 attempts for internet connectionen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectPerformanceen_US
dc.subjectNetworken_US
dc.titlePerformance rate for implementation of mobile learning in networken_US
dc.typeArticleen_US


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